t-Distributed Stochastic Neighbor Embedding (t-SNE) is a powerful dimensionality reduction technique primarily used for visualizing high-dimensional datasets. Unlike traditional methods like Principal Component Analysis (PCA), which focus on maximizing variance, t-SNE emphasizes preserving the local structure of the data. It achieves this by constructing probability distributions that represent the similarities between data points in high-dimensional space and then minimizing the divergence between these distributions in a lower-dimensional embedding. This results in a visual representation where similar data points are clustered closely together, making it easier to identify patterns and relationships within complex datasets.